Changelog:
– 13 November 2019: add prominent note to use module load gcc-7.1.0 on department machines

Task

1. Download rotate.tar  on a Linux system.
2. Run tar xvf rotate.tar to extract the tarball, creating the directory rotate.
3. When running on department machines, run ‘module load gcc-7.1.0‘ before compiling code for rotate, in order to select the same compiler we will be using for testing.
4. Edit rotate.c to make the who_t structure to include the your name and computing ID. You also need to supply a scoreboard name. We will publicly post the last performance results on a scoreboard, which will use whatever scoreboard name you supply. (There will also be a more detailed result viewer only visible to you.)Note that unlike previous versions of this assignment, it is to completed individually; you may not work with a partner.
5. In rotate.c, create new implementations of rotate that perform the same calculation as naive_rotate, but will be faster. Modify register_rotate_functions to include each of these functions.You may not attempt to interfere with the code that will time your new implementations of rotate. (See “other rules” below.)Your functions only need to work for multiple-of-32 dimension values.
6. Test your implementation to make sure it is correct and to see what average speedup it gets. To get full credit, your speedup should be at least 2.7 on our testing machine. (See “grading” below for more details.)
   1. You can run make to build ./benchmark to test your rotate implementation on your own machine or on a department machine. On the department machines, you can get access to the same compiler we will use to test your submission by running module load gcc-7.1.0. Usually, if version A of rotate is significantly faster on your machine, it will also be faster on ours, but the amount faster may vary significantly. The most notable exception is if your machine has a different compiler (e.g. gcc is actually some version of Clang and not some version of GCC, like on a typical OS X machine); one way to mitigate this effect by testing on the department servers.You should always do this before submitting something for our server to run to make sure your new version of rotate performs the correct calculation.
   2. If you submit rotate.c to kytos, we will time it on our machine and post the results here. It may take 30-60 minutes for us to run your submission, and perhaps more if we receive a lot of submissions in a short amount of time.

Collobaration Policy

The following kinds of conversations are permitted with people other than the course staff:

• drawings and descriptions of how you want to handle memory accesses across an image
• code snippets explaining optimizations, but only for code unlike the smooth and rotate problems we are discussing
• discussion of the code we provide you: how RIDX works, what the naive implementations are doing, etc.

Other rules

Violations of the following rules will be seen as cheating, subject to penalties that extend beyond the scope of this assignment:

• You must not modify or interfere with the timing code
• You must not attempt to inspect or modify the network, disk, or other grading platform resources

Additionally, the following rules may result in grade penalties within this assignment if violated:

• You must write valid C code. (But you may use GCC-specific extensions.)
• You should not turn in code that contains print statements.
• Your code must work (i.e., the same functionality as the provided naive implementations) for any image of any multiple-of-32 dimension (32, 64, 96, etc). It is okay, however, if you check the dimension passed into your code and have different versions run for different dimensions.
• You must not override command-line optimization options we pass to mostly prevent the compiler from generating vector instructions other than when vector intrinsic are used. That is, you must not use GCC extensions that allow C code in a file to temporarily change the command-line compiler optimizations flags in order to the make the optimizer generate more vector instructions for code that does not use vector intrinsics or otherwise explicitly identify how to use vector instructions.

Rotate Task Overview

This assignment deals with optimizing memory-intensive code. You will be optimizing an image processing operation we will call “rotate”, which rotates an image counter-clockwise by 90◦.

We will consider an image to be represented as a two-dimensional matrix M, where M_i,j denotes the value of (i, j)th pixel of M. Pixel values are triples of red, green, and blue (RGB) values. We will only consider square images. Let N denote the number of rows (or columns) of an image. Rows and columns are numbered, in C-style, from 0 to N − 1.

Given this representation, the rotate operation can be implemented quite simply as the combination of the following two matrix operations:

• Transpose: For each (i, j) pair, M_i,j and M_j,i are interchanged.
• Exchange rows: Row i is exchanged with row N − 1 − i.

This combination is illustrated in the following figure:

Note that:

• Performance of the baseline implementation is substantially worse at some sizes.
• Most benchmarked sizes have been chosen to be very large to emphasize certain kinds of performance effects.

Code

Structures we give you

A pixel is defined in defs.h as

typedef struct {
    unsigned char red;
    unsigned char green;
    unsigned char blue;
        unsigned char alpha;
} pixel;

(The “alpha” component represents transparency.)

In memory, this takes up 4 bytes, one byte for red, followed by one byte for green, and so on.

Images are provided in flattened arrays and can be accessed by RIDX, defined as

#define RIDX(i,j,n) ((i)*(n)+(j))

by the code nameOfImage[RIDX(index1, index2, dimensionOfImage)].

All images will be square and have a size that is a multiple of 32.

What you should change

In rotate.c you will see several rotate functions.

• naive_rotate which shows you what a naive implementation looks like. Your code must produce the same result as this.
• You may add as many other rotate methods as you want. You should put each new optimization idea in its own method: rotate_outer_loop_unrolled_3_times, rotate_with_2_by_3_blocking, etc. The driver will compare all your versions as long as you register them in the register_rotate_functions methods.

The source code you will write will be linked with object code that we supply into a benchmark binary. To create this binary, you will need to execute the command

    $ make

You will need to re-make the benchmark program each time you change code in rotate.c. To test your implementations, you can run the command:

    $ ./benchmark

If you want to only run a particular function, you can run

    $ ./benchmark 'foo'

to only run benchmark functions whose name contains “foo”.

Note that the benchmark results on your machine and the shared lab servers are often different
than our grading machine. Generally, we’ve found that optimizations that make code significantly
faster on other machines usually make code significantly faster on our grading machine. However, the amount
of speedup may be quite different.

Measuring on our grading machine

You can measure the performance on our grading machine by submitting a version of rotate.c
to kytos.

We will periodically scan for new submissions and run them on our grading server.

You can view detailed results here, which include the times for each version you submitted.

In addition, your last result will appear on a public scoreboard.

Correctness Testing

If one of your implementation produces a wrong answer, you can test it with the ./test program,
which will show you its complete input and output.

To run this, first build it by running

    $ make

then choose a size to test (e.g. 4), and to test your rotate function named rotate_bar use:

    $ ./test "rotate_bar" 4

The ./test program will run all test functions whose description contains the supplied string. For example,
the above command would run a function whose description was rotate_bar: version A
as well was one whose description was bad_rotate_bar_and_baz: version B.

Note that ./test can run your program on sizes that are not required to work. In particular, we do not
require your functions to work on non-multiple-of-32 sizes, but you may find sizes smaller than
32 convenient for debugging.

Grading

The benchmark program tests your program at several sizes, computes the speedup over the naive
code at each size, then takes the geometric mean of these results to get an overall speedup number.
This is what we will use to grade.
*
Speedups vary wildly by the host hardware. I have scaled the grade based on my timing server’s hardware so that particular strategies will get 75% and 100% scores.

Rotate will each be weighted as a full homework assignment in gradebook.

Rotate will get 0 points for 1.0× speedups on my computer, 75% for 1.50×, and 100% for 2.7× speedups, as expressed in the following pseudocode:

if (speedup < 1.00) return MAX_SCORE * 0;
if (speedup < 1.60) return MAX_SCORE * 0.75 * (speedup - 1.0) / (1.60 - 1.0);
if (speedup < 2.70) return MAX_SCORE * (0.75 + 0.25 * (speedup - 1.60) / (2.70 - 1.60));
return MAX_SCORE;

If you submit many times, your grade will be based on the version submitted that results
in the best score, taking late penalties into account.

About our Testing Server

We will be testing the performance of this program on our machine. We will be build your programs with GCC 7.1.0, which is available on portal.cs.virginia.edu or via NX remote desktop after running module load gcc-7.1.0. For this compiler gcc --version outputs gcc (GCC) 7.1.0. We will compile your submitted files using the options -g -Wall -O2 -std=gnu99 -mavx2 -mtune=skylake -fno-strict-aliasing -fno-tree-vectorize.

Our testing server has an Intel “Kaby Lake” processor with the following caches:

• A 32KB, 8-way set associative L1 data cache;
• A 32KB, 8-way set associative L1 instruction cache;
• A 256KB, 4-way set associative L2 cache, shared between instructions and data;
• A 8MB, 16-way set associative L3 cache, shared between instructions and data;

The size of a cache block in each of these caches is 64 byte.

Things about our processor that some students might want to know but probably aren’t that important:

• Our processor also has a 4-way set associative 64-entry L1 data TLB, an 8-way set associative 128-entry L1 instruction TLB, and an 6-way set associative 1536-entry L2 TLB (shared between instructions and data).

Hints

Kinds of optimizations we discussed

The following is a non-comprehensive list of optimization techniques we mentioned, which may or may not be applicable to this assignment:

• changing loop orders (see cache performance lectures);
• cache blocking, with various block sizes in each dimension (see cache performance lectures);
• making sure reused values are kept in registers by using temporary variables;
• using temporary variables so the compiler can know there is no aliasing, including the special case of “register blocking”;
• loop unrolling, with various amounts of unrolling;
• function inlining;
• using multiple accumulators;
• simplifying address calculations;
• using instructions that act on more data at once (e.g. instructions that load/store multiple values at once or add multiple values at once), like the SIMD instructions;
• removing redundant operations from a loop;

Miscellaneous optimization hints

• For optimizations that attempt to benefit from the cache, it may be helpful to consider what sequences of addresses are being accessed in iterations of the inner loops. Changing the order of loops and/or cache blocking can help improve or at least change cache performance
• Some optimizations you may want to try have parameters to choose, like the number of times to unroll the loop or the order in which to go through matrix indices. We suggest trying several values of the parameter — it may be the case that the optimization appears not to work but you just choose a much too high or too low parameter. Note that you can submit versions with multiple values of a parameter.
• Loop unrolling combines effectively with other optimizations like multiple accumulators effectively
• For a loop with a fixed number of iterations, loop unrolling is sometimes a lot more effective if you can eliminate the loop entirely. (For example, replacing an 8-iteration loop with 8 copies of the loop body.)
• Sometimes the compiler will generate unnecessarily complicated address calculation code. This can
  • performing multiplications to compute addresses when the address could be computed by
    adding to the address from the previous iteration;
  • adding to addresses computed in the previous iterations instead of multiplying, but
    storing addresses on the stack to do so instead of adding to related addresses
    from the same iteration, etc.;

  If you have evidence that this is occuring, a fix can be to write more explicit address computation code that

  • avoids most multiplications in computing addresses; and/or
  • compute the address of one element by adding to the address of the previous as in

      int *x = &array[i * N + j];
      int *y = x + N; /* instead of &array[(i+1) * N + j] */
    

• We provide you with performance numbers at each of the sizes we test. Observe thatsome optimizations may have little effect on certain sizes, but very large effects on others.
• It is permissible for your function to use different techniques depending on the input size.